metrics

ROC与AUC的定义与使用详解

生来就可爱ヽ(ⅴ<●) 提交于 2019-11-28 04:05:33
分类模型评估: 指标 描述 Scikit-learn函数 Precision 精准度 from sklearn.metrics import precision_score Recall 召回率 from sklearn.metrics import recall_score F1 F1值 from sklearn.metrics import f1_score Confusion Matrix 混淆矩阵 from sklearn.metrics import confusion_matrix ROC ROC曲线 from sklearn.metrics import roc AUC ROC曲线下的面积 from sklearn.metrics import auc 回归模型评估: 指标 描述 Scikit-learn函数 Mean Square Error (MSE, RMSE) 平均方差 from sklearn.metrics import mean_squared_error Absolute Error (MAE, RAE) 绝对误差 from sklearn.metrics import mean_absolute_error, median_absolute_error R-Squared R平方值 from sklearn.metrics import r2

OneAPM大讲堂 | Metrics, Tracing 和 Logging 的关系

主宰稳场 提交于 2019-11-28 03:03:46
【编者按】这是在 OpenTracing 和分布式追踪领域内广受欢迎的一片博客文章。在构建监控系统时,大家往往在这几个名词和方式之间纠结。 通过这篇文章,作者很好的阐述了分布式追踪、统计指标与日志之间的区别和关系。 Peter Bourgon 原作: Metrics, tracing, and logging 译者:吴晟 正文 今天,我很荣幸的参加了 2017 分布式追踪峰会(2017 Distributed Tracing Summit), 并和来自 AWS/X-Ray, OpenZipkin, OpenTracing, Instana, Datadog , Librato,以及其他更多组织的同仁进行了愉快的沟通和讨论。 其中一个重要的论点,是针对监控项目的范围和定义的。作为一个分布式追踪系统,应该管理日志么?从不同角度看来,到底什么是日志?如何通过一张图形象的定位这些形形色色的系统? 总体说来,我觉得我们是在一些通用的名词间纠结。我想我们可以通过图表来定义监控的作用域,使各名词的作用范围更明确。 我们使用维恩图(Venn diagram)来描述 Metrics, Tracing, Logging 三个概念的定义。他们三者在某些情况下是重叠的,但是我尽量尝试定义他们的不同。如下图所示: Metrics 的特点是,它是可累加的:他们具有原子性,每个都是一个逻辑计量单元

How to count lines of Java code using IntelliJ IDEA?

我怕爱的太早我们不能终老 提交于 2019-11-28 02:59:40
I know I've used the feature in the past, but I have no idea how I did this before. It must be something simple, right? larham1 The Statistic plugin worked for me. To install it from Intellij: File - Settings - Plugins - Browse repositories... Find it on the list and double-click on it. Open statistics window from: View -> Tool Windows -> Statistic Neil Quick and dirty way is to do a global search for '\n' . You can filter it any way you like on file extensions etc. Ctrl - Shift - F -> Text to find = '\n' -> Find. Edit: And 'regular expression' has to be checked. Dan Dyer In the past I have

Is there an optimizer in keras based on precision or recall instead of loss?

柔情痞子 提交于 2019-11-28 00:55:35
问题 I am developping a segmentation neural network with only two classes, 0 and 1 (0 is the background and 1 the object that I want to find on the image). On each image, there are about 80% of 1 and 20% of 0. As you can see, the dataset is unbalanced and it makes the results wrong. My accuracy is 85% and my loss is low, but that is only because my model is good at finding the background ! I would like to base the optimizer on another metric, like precision or recall which is more usefull in this

Mythical man month 10 lines per developer day - how close on large projects? [closed]

萝らか妹 提交于 2019-11-27 19:40:20
问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . Locked . This question and its answers are locked because the question is off-topic but has historical significance. It is not

Flink 源码解析 —— Standalone Session Cluster 启动流程深度分析之 Job Manager 启动

白昼怎懂夜的黑 提交于 2019-11-27 19:09:11
Job Manager 启动 https://t.zsxq.com/AurR3rN 博客 1、 Flink 从0到1学习 —— Apache Flink 介绍 2、 Flink 从0到1学习 —— Mac 上搭建 Flink 1.6.0 环境并构建运行简单程序入门 3、 Flink 从0到1学习 —— Flink 配置文件详解 4、 Flink 从0到1学习 —— Data Source 介绍 5、 Flink 从0到1学习 —— 如何自定义 Data Source ? 6、 Flink 从0到1学习 —— Data Sink 介绍 7、 Flink 从0到1学习 —— 如何自定义 Data Sink ? 8、 Flink 从0到1学习 —— Flink Data transformation(转换) 9、 Flink 从0到1学习 —— 介绍 Flink 中的 Stream Windows 10、 Flink 从0到1学习 —— Flink 中的几种 Time 详解 11、 Flink 从0到1学习 —— Flink 读取 Kafka 数据写入到 ElasticSearch 12、 Flink 从0到1学习 —— Flink 项目如何运行? 13、 Flink 从0到1学习 —— Flink 读取 Kafka 数据写入到 Kafka 14、 Flink 从0到1学习 ——

Flink 源码解析 —— Standalone session 模式启动流程

瘦欲@ 提交于 2019-11-27 19:08:31
Standalone session 模式启动流程 https://t.zsxq.com/EemAEIi 博客 1、 Flink 从0到1学习 —— Apache Flink 介绍 2、 Flink 从0到1学习 —— Mac 上搭建 Flink 1.6.0 环境并构建运行简单程序入门 3、 Flink 从0到1学习 —— Flink 配置文件详解 4、 Flink 从0到1学习 —— Data Source 介绍 5、 Flink 从0到1学习 —— 如何自定义 Data Source ? 6、 Flink 从0到1学习 —— Data Sink 介绍 7、 Flink 从0到1学习 —— 如何自定义 Data Sink ? 8、 Flink 从0到1学习 —— Flink Data transformation(转换) 9、 Flink 从0到1学习 —— 介绍 Flink 中的 Stream Windows 10、 Flink 从0到1学习 —— Flink 中的几种 Time 详解 11、 Flink 从0到1学习 —— Flink 读取 Kafka 数据写入到 ElasticSearch 12、 Flink 从0到1学习 —— Flink 项目如何运行? 13、 Flink 从0到1学习 —— Flink 读取 Kafka 数据写入到 Kafka 14、 Flink

Good PHP Metric tools [closed]

匆匆过客 提交于 2019-11-27 18:22:36
I have been coding in PHP for a while using Netbeans but it does not provide any tools for obtaining code metrics. I have also used SourceMonitor before but it does not support PHP, same with Code Analyzer . Has anyone used and can recommend any tools for getting code metrics from PHP code? My company provides a variety of PHP tools for measuring software quality, both statically and dynamically. The SD Source Code Search Engine is an interactive GUI that allows you to search across large bodies of source code (e.g., PHP and HTML) quickly and easily. It provides fast searches by indexing the

Lucene fieldNorm discrepancy between Similarity calculation and query-time value

有些话、适合烂在心里 提交于 2019-11-27 16:55:19
问题 I'm trying to understand how fieldNorm is calculated (at index time) and then used (and apparentlly re-calculated) at query time. In all the examples I'm using the StandardAnalyzer with no stop words. Deugging the DefaultSimilarity 's computeNorm method while indexing stuff, I've noticed that for 2 particular documents it returns: 0.5 for document A (which has 4 tokens in its field) 0.70710677 for document B (which has 2 tokens in its field) It does this by using the formula: state.getBoost()

Calculate code metrics [closed]

可紊 提交于 2019-11-27 16:40:27
Are there any tools available that will calculate code metrics (for example number of code lines, cyclomatic complexity, coupling, cohesion) for your project and over time produce a graph showing the trends? aku On my latest project I used SourceMonitor . It's a nice free tool for code metrics analysis. Here is an excerpt from SourceMonitor official site: Collects metrics in a fast, single pass through source files. Measures metrics for source code written in C++, C, C#, VB.NET, Java, Delphi, Visual Basic (VB6) or HTML. Includes method and function level metrics for C++, C, C#, VB.NET, Java,